GEDI is a modern full-waveform LiDAR mission, operating from the International Space Station, designed to capture vertical vegetation structure at global scale. Similarly, the ICESat-2 launched by NASA in 2018 has received extensive attention for forest canopy height mapping. However, the measurements from space-borne LiDAR sensors are affected by undulating mountainous terrain, making them less accurate. This study attempts to develop a canopy height model in the Western Himalayan Region by integrating observations from GEDI and ICESat-2. A combination of multispectral, hyperspectral and backscatter datasets were used as predictive variables. Variable selection results suggest higher significance of optical vegetation indices as compared to backscatter variables. The accuracy of canopy height models in the Himalayan region can be improved by selecting high-quality LiDAR observations and identifying appropriate predictive variables. The integration of hyperspectral data for modeling forest variables is constrained by the limited spatial and temporal coverage of hyperspectral datasets.